A model based on machine learning for the prediction of cyclosporin A trough concentration in Chinese allo-HSCT patients

医学 机器学习 槽水位 低谷(经济学) 治疗药物监测 治疗窗口 钙调神经磷酸酶 人口 药品 内科学 人工智能 药理学 移植 他克莫司 计算机科学 环境卫生 宏观经济学 经济
作者
Lin Song,Chenrong Huang,Shi-Zheng Pan,Jianguo Zhu,Zong-Qi Cheng,Xun Yu,Ling Xue,Fan Xia,Jinyuan Zhang,Depei Wu,Liyan Miao
出处
期刊:Expert Review of Clinical Pharmacology [Informa]
卷期号:16 (1): 83-91 被引量:6
标识
DOI:10.1080/17512433.2023.2142561
摘要

Cyclosporin A is a calcineurin inhibitor which has a narrow therapeutic window and high interindividual variability. Various population pharmacokinetic models have been reported; however, professional software and technical personnel were needed and the variables of the models were limited. Therefore, the aim of this study was to establish a model based on machine learning to predict CsA trough concentrations in Chinese allo-HSCT patients.A total of 7874 cases of CsA therapeutic drug monitoring data from 2069 allo-HSCT patients were retrospectively included. Sequential forward selection was used to select variable subsets, and eight different algorithms were applied to establish the prediction model.XGBoost exhibited the highest prediction ability. Except for the variables that were identified by previous studies, some rarely reported variables were found, such as norethindrone, WBC, PAB, and hCRP. The prediction accuracy within ±30% of the actual trough concentration was above 0.80, and the predictive ability of the models was demonstrated to be effective in external validation.In this study, models based on machine learning technology were established to predict CsA levels 3-4 days in advance during the early inpatient phase after HSCT. A new perspective for CsA clinical application is provided.
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